K-Means

Higher-level K-Means functions.

KMeansBuffers{T}(; n_dims::Integer, max_k::Integer, n_points::Integer)::KMeansBuffers where {T<: AbstractFloat}
KMeansBuffers(buffers::KMeansBuffers{T}; n_dims::Integer, k::Integer, n_points::Integer) where {T <: AbstractFloat}

Pre-allocate buffers for allocation-free K-Means computation. Create a smaller view sharing the storage with KMeansBuffers(buffers; n_dims, k, n_points) .

KmeansResultView

A view into KMeansBuffers that provides the same accessor interface as Clustering.KmeansResult ( assignments , counts , wcounts , nclusters , totalcost ) without allocating.

kmeans_in_buffers(
    X::AbstractMatrix{<:Real},
    k::Integer;
    buffers::Maybe{KMeansBuffers} = ```nothing```,
    maxiter::Integer = ```100```,
    tol::Real = ```1.0e-6```,
    distance::SemiMetric = ```Distances.SqEuclidean```,
    rng::AbstractRNG = default_rng(),
)::Union{KmeansResult, KmeansResultView}

Same as kmeans , but if buffers are specified, run allocation-free code. Seeding is restricted to :kmpp . Implementation is otherwise identical to `Clustering.kmeans.

kmeans_in_buffers!(
    X::AbstractMatrix{<:Real},
    centers::AbstractMatrix{<:AbstractFloat};
    buffers::Maybe{KMeansBuffers} = ```nothing```,
    maxiter::Integer = ```100```,
    tol::Real = ```1.0e-6```,
    distance::SemiMetric = ```Distances.SqEuclidean```,
    rng::AbstractRNG = default_rng(),
)::Union{KmeansResult, KmeansResultView}

Same as kmeans! , but if buffers are specified, run allocation-free code. Seeding is restricted to :kmpp . Implementation is otherwise identical to `Clustering.kmeans!.

kmeans_in_rounds(
    values_of_points::AbstractMatrix{<:AbstractFloat},
    k::Integer;
    centers::Maybe{AbstractMatrix{<:AbstractFloat}} = ```nothing```,
    buffers::Maybe{Tuple{KMeansBuffers, KMeansBuffers}} = ```nothing```,
    rounds::Integer = ```10```,
    rng::AbstractRNG = default_rng(),
)::Union{KmeansResult, KmeansResultView}

Run kmeans multiple times with different random seeds (using rng ) and return the best results. This is needed because K-Means is a heuristic and tends to occasionally get stuck in a local minimum.

If buffers are specified, runs allocation-free using kmeans_in_buffers / kmeans_in_buffers! . Otherwise (the default), falls back to using Clustering.kmeans / Clustering.kmeans! .